Academically a - very - interesting article, which confirms my ongoing suspicion of LLM Benchmarks in general ( probably why I like it so much). Another thing that caught my eye is DeepSWE having a bunch of bugs in them. The more I learn about these benchmarks the more sceptical I become. I would expect such a prolific benchmark to have these bugs ironed out by now.
The practical takeaway, however, seems to be an interesting "don't use max mode". The passage about deepseeks weird API behavior dropping to pretty much zero the moment you used high says a lot.
Also, personally, even though Opus is clearly so much ahead of the Open Source models after the changes, I think the adjusted score is still very much competitive. I see other commenters using this as proof to justify why their opinions of the open source models - but a drop from 74% to ~50% is, while a big drop, still about a ~50% pass rate afterwards. That's far from useless for coding, especially since DeepSWE seems to be such a difficult benchmark. It simply seems to more accurately reflect the capabilities of these models.
All in all, I'd love to see more of such articles, and will probably keep an eye on this one - if they continue "benchmarking" these models this way.
AI Labs actually. They would love the perfect benchmar representing real usecase. I'd even dare and guess that they don't share much publically because it is a huge competitive advantage.
The big trend in that graph is that smaller models collapse the most. Everything with "-flash" in the name, plus OpenAI Luna.
Look at GPT 5.6-Sol vs. GPT 5.6-Luna in particular.
(The GLM pair shouldn't be expected follow this pattern, because they're misnamed: GLM-5.3 and GLM-5.3-flash are distinct lineages. The -flash is an early release of a new pretrain on a new architecture).
If this *is* the main factor, I'd expect Kimi K3.1 and Gemini Carbon would also do well on this test (unlike Gemini-flash); while Haiku 6 would collapse (breaking the trend of the other recent Claude models). It's a falsifiable prediction.
late edit to add: Here's a scatterplot to illustrate what I *speculate* is happening here ("(?)" means parameter counts are "community estimates"; i.e. garbage data; but they're on a log scale anyway),
One thing to consider is that even when some model has big gap between those two benchmark it doesn't mean it is much worse. It could be worse becasue he has assuptions based on training but on real scenario it wouldn't have it so it still could achive great results
DeepSWE was released on May and Deepseek v4 flash was released on April. By definition it couldn't be benchmaxxed on this particular benchmark. My guess is the mutation is not like real world and it is tripping small models, not benchmaxed model.
It's not clear which v4 flash they used. There are 3 different releases for that one.
Edit: I think that's the answer. This page lists the score as 53.3% which is close to the 54.4% reported here https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-0731 for the 0731 update. The original from April was called preview, doesn't list the score, but in the comparison with the update it scores 7.3%.
> Most models tried pleasing an imagined grader over the prompt
>When we read the trajectories, we saw that many models had figured out they were in a benchmark and started second-guessing the prompt. Instead of just building what we asked for, they started coding to the tests they thought the grader would run.
I was beginning to worry I had overlooked the inner beauty of DeepSeek. When I have tried it, it was totally useless. Never understood HN cheering for it.
Don't know what you're doing, but it's good enough for the reasonably sized tasks I give LLMs. It's also a much better price. My browser stopped rendering the UI frame recently because I messed up permissions on /usr/share/icons. Took DS 4.1 flash about 3 minutes and a few cents to diagnose it. Same harness with opus 5 took twice as long and several dollars.
Moreover, the months I've used anthropic/OAI exclusively my API token costs are in the hundreds (or sometimes thousands). With DS flash as the first agent I reach for, I rarely exceed $40/mo.
I figured it was assumed I was including that? It's been fine as long as I break tasks up into reasonably sized chunks, which is fine because I think you should only be generating as much as you can comfortably review at a time.
I've had significant success using DS 4 Pro and now 4.1 flash for fairly complex coding tasks on C++, Rust and Python projects at home.
I have access to Claude Code at work (and use it quite a bit), so I feel I can honestly compare them.
You do have to plan things first a bit more with DS (than say Opus can (Opus will have more chance of one-shotting something, although I generally don't think that's a good idea), although it often over-engineers and tries to be too helpful in my experience) and be a bit more explicit at times, but I feel it's quite close to Sonnet in performance for implementing plans, debugging issues and explaining how codebases work.
This has always been a problem with the Internet. When the iPad was released, anti-Apple subreddits talked about what a garbage device it was. Wishful thinking dominates a lot of human content generators. Extracting information from humanslop is harder than avoiding it entirely.
Hard to say if you don't share what kind of project, workflow and prompt you're trying tbh. I've been driving it mainly to write a quite big golang and android app and most of the tasks are all handled fine by DeepSeek.
Assume about 50% of internet writing has zero tether to reality and is written out of pure motivation to shape a preferred narrative (and usually the same one).
When you see this your worldview will instantly become much clearer.
Not everyone is operating with the same constraints. As for me, once the models got good enough (~opus 4.5 levels of intelligence) that they could solve most problems that I was dealing with, I stopped following the frontier intelligence trajectory for daily use and instead followed constant intelligence and decreasing cost. Nowadays I primarily use GPT 6.1 luna, and DeepSeek 4.1 flash.
I've also been very happy playing around with qwen 3.8 27b running locally for personal projects. It sometimes can't quite get there, and that's when I'll reach for GPT.
> There is always a huge discrepancy between benchmarks and real world capabilities.
I mean of course, the real world is messy, the models are only good for what they've been trained on, and post-training frequently takes benchmark scores into account too, leading to this sort of "Great score, sucks in the real world" syndrome that seemingly most models are affected by.
The practical takeaway, however, seems to be an interesting "don't use max mode". The passage about deepseeks weird API behavior dropping to pretty much zero the moment you used high says a lot.
Also, personally, even though Opus is clearly so much ahead of the Open Source models after the changes, I think the adjusted score is still very much competitive. I see other commenters using this as proof to justify why their opinions of the open source models - but a drop from 74% to ~50% is, while a big drop, still about a ~50% pass rate afterwards. That's far from useless for coding, especially since DeepSWE seems to be such a difficult benchmark. It simply seems to more accurately reflect the capabilities of these models.
All in all, I'd love to see more of such articles, and will probably keep an eye on this one - if they continue "benchmarking" these models this way.
Look at GPT 5.6-Sol vs. GPT 5.6-Luna in particular.
(The GLM pair shouldn't be expected follow this pattern, because they're misnamed: GLM-5.3 and GLM-5.3-flash are distinct lineages. The -flash is an early release of a new pretrain on a new architecture).
If this *is* the main factor, I'd expect Kimi K3.1 and Gemini Carbon would also do well on this test (unlike Gemini-flash); while Haiku 6 would collapse (breaking the trend of the other recent Claude models). It's a falsifiable prediction.
https://files.catbox.moe/gryjzm.png
Edit: I think that's the answer. This page lists the score as 53.3% which is close to the 54.4% reported here https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-0731 for the 0731 update. The original from April was called preview, doesn't list the score, but in the comparison with the update it scores 7.3%.
>When we read the trajectories, we saw that many models had figured out they were in a benchmark and started second-guessing the prompt. Instead of just building what we asked for, they started coding to the tests they thought the grader would run.
I ran into the same problem on my English exams.
For my use case deepseek 4.1 flash is almost as dumb as haiku 5.5. Both are fast garbage producers.
Moreover, the months I've used anthropic/OAI exclusively my API token costs are in the hundreds (or sometimes thousands). With DS flash as the first agent I reach for, I rarely exceed $40/mo.
I have access to Claude Code at work (and use it quite a bit), so I feel I can honestly compare them.
You do have to plan things first a bit more with DS (than say Opus can (Opus will have more chance of one-shotting something, although I generally don't think that's a good idea), although it often over-engineers and tries to be too helpful in my experience) and be a bit more explicit at times, but I feel it's quite close to Sonnet in performance for implementing plans, debugging issues and explaining how codebases work.
When you see this your worldview will instantly become much clearer.
I've also been very happy playing around with qwen 3.8 27b running locally for personal projects. It sometimes can't quite get there, and that's when I'll reach for GPT.
I mean of course, the real world is messy, the models are only good for what they've been trained on, and post-training frequently takes benchmark scores into account too, leading to this sort of "Great score, sucks in the real world" syndrome that seemingly most models are affected by.
Edit: another comment pointed out it's probably not benchmaxxing due to the chronology.
https://news.ycombinator.com/item?id=50406948
But why should minor changes to the task cause catastrophic performance drops? How is that not evidence of benchmaxxing?